Identity recognition method and device

ABSTRACT

The present specification provides an identity recognition method and device. The method comprises: collecting data of address books, each address book comprising multiple identity information pairs of multiple users, and each identity information pair comprising a name and a mobile phone number; searching for an identity information pair to be recognized in the data of address books, the identity information pair to be recognized comprising a name and a mobile phone number of a user to be recognized; and in response to that the searching result satisfies a risk condition, determining that the user to be recognized has a risk.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of International PatentApplication No. PCT/CN2017/102213, filed on Sep. 19, 2017, which isbased on and claims priority to the Chinese Patent Application No.201610851175.2, filed on Sep. 26, 2016 and entitled “IdentityRecognition Method and Device.” The above-referenced applications areincorporated herein by reference in their entirety.

TECHNICAL FIELD

The present disclosure relates to network technologies, and inparticular, to an identity recognition method and device.

BACKGROUND

Real-name registration is often requested for access to Internet.Real-name authentication is therefore needed in many Internet scenarios,in particular, in services such as finance and e-commerce. To hide theirtrue identities, swindlers who cheat and conduct fraudulence oftenobtain a lot of other people's identity information through leakage ofinformation on the Internet or through volume purchase, assume the otherpeople's ID numbers and names, and use obtained mobile phone numbers foraccount registration and authentication on Internet, committingfraudulence in credit applications such as credit card or loanapplications, thereby causing losses of service providers and financialinstitutions.

In existing identity authentication manners, fraudulence is recognizedmainly on a network layer or device layer. For example, identity theftmay be recognized by using a recognition model based on the IP address,MAC address, or device identifier like IMEI, of the device used by theperson who steals the identity. However, many of the swindlers areprofessional hackers who have strong network skills and can bypass theexisting identity recognition models through some strategies and make itdifficult to recognize identities.

SUMMARY

The present disclosure provides an identity recognition method, device,and a non-transitory computer-readable storage medium to achievedetection of identity fraud.

According to one aspect, the identity recognition method may comprise:collecting data of address books, each address book comprising multipleidentity information pairs of multiple users, and each identityinformation pair comprising a name and a mobile phone number; searchingfor an identity information pair to be recognized in the data of addressbooks, the identity information pair to be recognized comprising a nameand a mobile phone number of a user to be recognized; and in response tothat the searching result satisfies a risk condition, determining thatthe user to be recognized has a risk.

In some embodiments, the method may further comprise: determining aweight corresponding to each identity information pair, the weightindicating a degree of credibility of the identity information pair.

In other embodiments, the determining a weight corresponding to eachidentity information pair may comprise: determining a weightcorresponding to each identity information pair based on the number ofaddress books comprising the identity information pair.

In still other embodiments, the determining a weight corresponding toeach identity information pair may comprise: calculating a pagerankvalue of each identity information pair using a pagerank method, andusing the pagerank value as the weight of the identity information pair.

In yet other embodiments, the calculating a pagerank value of eachidentity information pair may comprise: determining one or more linksconnecting each identity information pair with other identityinformation pairs based on the data of address books; and calculatingthe pagerank value of the each identity information pair based on thenumber of the one or more links.

In other embodiments, the method may further comprise: calculating thepagerank value of the each identity information pair further based onthe weights of the other identity information pairs connected with theeach identity information pair by the one or more links.

In still other embodiments, in response to that the searching resultsatisfies a risk condition, determining that the user to be recognizedhas a risk may comprise: in response to that the identity informationpair to be recognized is found in the data of address books and has aweight lower than a threshold, determining that the user to berecognized has a risk.

In yet other embodiments, in response to that the searching resultsatisfies a risk condition, determining that the user to be recognizedhas a risk may comprise: in response to that the identity informationpair to be recognized is not found in the data of address books,determining that the user to be recognized has a risk.

In other embodiments, the method may further comprise: correctinginconsistency among identity information pairs in different addressbooks.

According to another aspect, the identity recognition device maycomprise: one or more processors and one or more non-transitorycomputer-readable memories coupled to the one or more processors andconfigured with instructions executable by the one or more processors tocause the device to perform operations comprising: collecting data ofaddress books, each address book comprising multiple identityinformation pairs of multiple users, and each identity information paircomprising a name and a mobile phone number; searching for an identityinformation pair to be recognized in the data of address books, theidentity information pair to be recognized comprising a name and amobile phone number of a user to be recognized; and in response to thatthe searching result satisfies a risk condition, determining that theuser to be recognized has a risk.

According to still another aspect, provided is the non-transitorycomputer-readable storage medium storing instructions that, whenexecuted by a processor, cause the processor to perform operationscomprising: collecting data of address books, each address bookcomprising multiple identity information pairs of multiple users, andeach identity information pair comprising a name and a mobile phonenumber; searching for an identity information pair to be recognized inthe data of address books, the identity information pair to berecognized comprising a name and a mobile phone number of a user to berecognized; and in response to that the searching result satisfies arisk condition, determining that the user to be recognized has a risk.

The identity recognition method and device according to embodiments ofthe present disclosure establish an identity information database bycollecting big data of address books, search for an identity informationpair to be recognized in the identity information database, the identityinformation pair to be recognized including a name and a mobile phonenumber of a user to be recognized, and determine whether the identityinformation pair of the name and the mobile phone number is authentic,thereby determining whether the to-be-recognized user's identity isfraudulent and thus detecting identity fraud.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of an example of an identity recognition methodaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram of big data of address books of usersaccording to some embodiments of the present disclosure;

FIG. 3 is a flow chart of another example of the identity recognitionmethod according to some embodiments of the present disclosure;

FIG. 4 is a schematic structural diagram of an example of an identityrecognition device according to some embodiments of the presentdisclosure;

FIG. 5 is a schematic structural diagram of another example of theidentity recognition device according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

The embodiments of the present disclosure provide an identityrecognition method used to recognize identity fraud. For example,swindlers assume other people's ID numbers and names, and use obtainedmobile phone numbers for account registration and authentication on theInternet, committing fraudulence in credit applications such as creditcard or loan applications. To recognize identity fraud even whenswindlers bypass recognition models on a network device layer, thepresent disclosure provides a recognition scheme that determines whethera mobile phone number used by a user is the mobile phone number normallyused by the user.

Based on the recognition scheme, after obtaining a sufficient amount ofaddress books of users, an identity recognition entity which is toperform identity recognition on customers may obtain mobile phonenumbers of all or nearly all potential customers to form an address bookdatabase. If subsequently a customer whose identity is to be verified isnot in the address book database or has a very low weight when appearingin the database, then it is very likely that it is not the customerhimself/herself that uses the identity, and the person using thecustomer's identity tends to be an imposter.

Referring to FIG. 1, an example of an identity recognition methodaccording to some embodiments of the present disclosure is shown. Themethod can include the following steps.

Step 101, collecting big data of address books, each address bookincluding multiple identity information pairs of multiple users, andeach identity information pair comprising a name and a mobile phonenumber.

For example, the big data of address books can include data of addressbooks from many users. FIG. 2 illustrates big data of address books ofusers, e.g., user 1, user 2, user 3 until user y. The amount of addressbooks is large enough to cover as many potential business customers aspossible, so that the data of the address books can be used to performidentity verification on customers subsequently. Each address book mayinclude multiple identity information pairs, and each identityinformation pair may include a name and a mobile phone number of a user.Taking the address book of user 1 as an example, “name N11-number P11”is an identity information pair indicating that the mobile phone numberused by the person or entity represented by the name “N11” is “P11.”Similarly, “name N12-number P12” is another identity information pairindicating that the mobile phone number used by the person or entityrepresented by the name “N12” is “P12.”

In this Step 101, the data of address books can be collected through avariety of manners. For example, data of an address book on a user'smobile phone can be collected through client software running on theuser's mobile phone.

Step 102, searching for an identity information pair to be recognized inthe big data of address books, the identity information to be recognizedincluding a name and a mobile phone number of a user to be recognized.

A searching result in this Step 102, for example, can include whetherthe big data of address books includes an identity information pair thatis the same as the identity information pair to be recognized, or thenumber of the identity information pairs in the big data of addressbooks that are the same as the identity information pair to berecognized, etc.

Step 103, if the searching result satisfies a risk condition,determining that the user is a user having a risk.

In some embodiments, the risk condition may be set as a variety ofconditions. For example, the risk condition can be set that a user to berecognized is a user having a risk if the big data of address books doesnot have an identity information pair that is the same as the identityinformation pair to be recognized. Alternatively, the risk condition canbe set that a user to be recognized has a risk if the big data ofaddress books includes one or more identity information pairs same asthe identity information pair to be recognized, but the number of theone or more identity information pairs same as the identity informationpair to be recognized is small, e.g., smaller than a pre-determinedthreshold.

The identity recognition method in the present example establishes anidentity information database by collecting big data of address books,search for an identity information pair to be recognized in the identityinformation database, the identity information pair to be recognizedcomprising a name and a mobile phone number of a user to be recognized,and determine whether the identity information pair of the name and themobile phone number is authentic, thereby determining whether theto-be-recognized user's identity is fraudulent and thus detectingidentity fraud.

Referring to FIG. 3, another example of the identity recognition methodis shown. The method shown in FIG. 3 constructs an information weighttable according to the big data of address books. The information weighttable can be used for verifying identities of users. As shown in FIG. 3,the method can include the following steps.

Step 301, collecting big data of address books. Step 302, performingstatistical analysis on the identity information pairs in the big dataof address books to obtain an information weight for each identityinformation pair, and generating an information weight table.

The information weight in this Step 301 can be used to indicate a degreeof credibility of a corresponding identity information pair. Forexample, if an identity information pair of “name N11-number P11”appears in many users' address books, then it is very likely that theinformation of the identity information pair is authentic andacknowledged by many users; otherwise, it may indicate that the identityinformation pair has a low degree of credibility and the information maybe falsified.

The information weights can be calculated according to differentmethods. Differences among the weights for different identityinformation pairs can be reflected by different statistics orrelationships among the identity information pairs in the address books.

For example, the number of address books that include an identityinformation pair can be counted and used as an information weight of theidentity information pair. Assuming that the identity information pairof “name N11-number P11” appears in five address books of users (such asfive users, each user has an address book), then the correspondinginformation weight can be five. Assuming that the identity informationpair of “name N12-number P12” appears in eight address books, then thecorresponding information weight can be eight.

In another example, a pagerank value of each identity information paircan be calculated according to a pagerank method, and the pagerank valueis used as an information weight of the identity information pair. Insome embodiments, according to the pagerank method, a graph model(similar to a web graph model in PageRank) may be built by using theidentity information pairs in the address books of users, and theinformation weight for each identity information pair may then becalculated based on the built graph model. When the graph model used bythe pagerank method is being constructed, each identity information paircan be used as a node (equivalent to the page node in PageRank), and anoutbound link of the page node points to another identity informationpair in the address book of the user to which the identity informationpair belongs. For example, the user to which the node of “nameN11-number P11” belongs is a user having the name of “N11,” the user'saddress book further includes the identity information pair of “nameN12-number P12,” and then an outbound link of the node of “nameN11-number P11” points to the node of “name N12-number P12.” An inboundlink of a page node comes from identity information pairs of users inaddress books that include the identity information pair correspondingto the page node. Similar to the example above, the inbound link of thenode of “name N12-number P12” is from the node of “name N11-number P11,”because the address book of the user in the node of “name N11-numberP11” includes the pair of “name N12-number P12.”

After the graph model is built, the pagerank method can be used tocalculate a pagerank value of each identity information pair (i.e.,node), and the pagerank value is used as an information weight of theidentity information pair. For example, the pagerank value (i.e., theinformation weight) of each identity information pair (i.e., node) maybe calculated based on the number of inbound links, outbound links, or acombination of inbound and outbound links connecting to the each node.

In a web graph model in PageRank, the more inbound links from otherwebpages a page node receives, the more important this page is. In theembodiments of the present disclosure, if an identity information pairis included in more address books, the identity information pair is morecredible. Therefore, the pagerank value (i.e., the weight) of anidentity information pair may be determined based on the number ofaddress books including the identity information pair, or the number ofinbound and/or outbound links from and/or to other identity informationpairs in the address books.

Further, in a web graph model in PageRank, different pages havedifferent qualities. A high quality page transfers a heavier weight toother pages via the links pointing to the other pages. Therefore, whenpages with higher quality point to another page, the other page is moreimportant. Accordingly, the pagerank value of a target identityinformation pair may be determined further based on the weights of theidentity information pairs pointing to the target identity informationpair. In the some embodiments, the impact of the user to which theaddress book having the identity information pair belongs may beconsidered. When the identity information pair appears in the addressbook of a well-known public figure, the degree of credibility of theinformation in the identity information pair may be different from itsdegree of credibility when the identity information pair appears in theaddress book of an unknown ordinary person.

An information weight table shown in Table 1 below can be generatedafter the calculation in this Step 302. As shown in Table 1, thegenerated information weight table includes identity information pairsand their information weights. In other embodiments, the identityinformation pairs and their information weights may be stored in a datastructure other than a table.

TABLE 1 Information weight table Identity information pairs Name NumberInformation weight N11 P11 t1 N12 P12 t2 . . . . . . . . .

In addition, there may be nonstandard records in the identityinformation pairs in an address book. For example, a user's real name is“Wang, Xiaoyue” e.g., “

” in Chinese. But when recording the user's name and mobile phonenumber, a friend of the user accidentally enters “

” [English translation: Wang, Xiaoyue] i.e., “

” [English translation: Xiao] is a typo. In some embodiments,inconsistency correction processing can be performed to correct theinconsistency that occurs when different users enter an originallyidentical identity information pair differently in their address books.For example, before the calculation of information weights for theidentity information pairs in the big data of address books, the pairsof “

[English translation: Wang, Xiaoyue]-number H” and “

[English translation: Wang, Xiaoyue]-number H” may both be treated asthe same pair of “wangxiaoyue-number H,” and recorded into theinformation weight table. That is, the inconsistent Chinese names “

” [English translation: Wang, Xiaoyue] and “

” [English translation: Wang, Xiaoyue] are treated as the same name inpinyin, and the information weight corresponding to the identityinformation pair of “wangxiaoyue-number H” can be 2 (because, forexample, “wangxiaoyue-number H” appears twice in the data of addressbooks). When an identity information pair to be recognized issubsequently compared with the information weight table, a matchingphone number, e.g., “H,” is first found according to the number in theidentity information pair to be recognized, and then the name in thepair is converted to pinyin to check if there is a matching name inpinyin. This way, the calculation of information weights can become moreaccurate. In some embodiments, the inconsistency correction processingmay be applied to other types of errors according to actual businesssituations.

In the above example, where the pinyin of the names is the same, theChinese characters of the names are different and the phone numbers arethe same, a pinyin character string can be recorded in the informationweight table to correct the inconsistency. In other embodiments, Chinesecharacters can be used to record the names in the information weighttable. To recognize an identity information pair, a matching number maybe found in the information weight table first according to the numberin the pair. Subsequently, it may be determined whether a matching namein Chinese character can be found, and if there is no matching name inChinese character, the name is converted to pinyin to check if there isa matching name in pinyin. When both the name and the number in the pairare matched, a matching identity information pair is found and acorresponding information weight can be obtained.

In still other embodiments, when searching for a matching identityinformation pair, inconsistency within a range may be allowed. Forexample, an identity information pair of “xiaoyue-number H” is recordedin the information weight table, i.e., the last name is missing, and theidentity information pair to be recognized is “

[English translation: Wang, Xiaoyue]-number H.” It may be found that thenumbers in these two identity information pairs are both “H,” and can bematched. Further, in the name field, “xiaoyue” is very similar to thepinyin of “

” [English translation: Wang, Xiaoyue], i.e., “wangxiaoyue”. Forexample, according to an algorithm, the similarity between the names iscalculated and reaches above a similarity threshold, e.g., 70%. Then itmay be determined that “xiaoyue” matches “

” [English translation: Wang, Xiaoyue]. In other embodiments, othervalues of the similarity threshold can be set and used. When thesimilarity between two records is higher than the threshold, the two areregarded as matching each other even though they are not identical.Otherwise, the records may not be regarded as matching. For example,with regard to “xiaoyue” and “Wang, Jiahui (

)” [English translation: Wang, Jiahui] the two names are substantiallydifferent and the similarity between them is lower than the threshold,and thus they are determined to be not matching.

The information weight table may be used in the following steps foridentity information recognition. An identity information pair to berecognized can be compared with the pre-generated information weighttable to obtain an information comparison result. The identityinformation pair to be recognized includes a name and a mobile phonenumber of a user to be recognized. If the information comparison resultsatisfies a risk condition, it is determined that the user is a userhaving a risk.

Step 303, obtaining an identity information pair of the user to berecognized.

For example, when a user is registering, identity information of theuser can be obtained to recognize whether the user is a defrauder whoassumes another person's identity. The identity information may includean ID number, a name, a mobile phone number, an address, and othercontact information, where the name and mobile phone number can bereferred to as an identity information pair.

Step 304, verifying the user's ID number and right to use the mobilephone number.

In this Step 304, the ID number and name can be verified through thepublic security network based on real names. Alternatively, a facialcomparison can be performed between the user's face and the photo on thepublic security network associated with the ID. In addition, theverification can be performed in other forms. Furthermore, the user'smobile phone number can be verified to ensure that the user owns theright to use the mobile phone number at present.

If the verification is passed in this Step 304, the method proceeds toStep 305; otherwise, the method proceeds to Step 309.

Step 305, querying the identity information pair of the user to berecognized from the information weight table.

If the identity information pair can be found in the information weighttable, the method proceeds to Step 306; otherwise, if the informationweight table does not include the identity information pair, the methodproceeds to Step 309.

Step 306, obtaining a corresponding information weight from theinformation weight table.

For example, an information weight corresponding to the identityinformation pair found in Step 303 can be obtained from thepre-generated information weight table.

Step 307, determining whether the information weight is greater than orequal to a weight threshold.

In some embodiments, the weight threshold, e.g., t0, can be setaccording to factors such as the coverage of all potential customers bythe amount of big data collected for generating the information weighttable, the strictness of the control of identity fraud risk by theentity using this identity recognition method, and the like. Forexample, assuming that the entity strictly controls users' identities,the weight threshold may be set at a large value to ensure highinformation authenticity and reliability. In another example, if theamount of the collected big data has a low coverage of all potentialcustomers, the weight threshold may be set at a large value to improvethe information authenticity and reliability.

If it is determined in this Step 307 that the information weight isgreater than or equal to a weight threshold, the method proceeds to Step308; otherwise, the method proceeds to Step 309.

Step 308, determining that the user to be recognized passes theverification and is a legitimate user.

Step 309, determining that the user to be recognized is a user having arisk.

After the user is determined to be a user having a risk, the fraudoperation of the user can be found accordingly.

The identity recognition method in the present example creates aninformation weight table according to big data of address books,determines credibility of each identity information pair, and candetermine, based on a weight threshold, whether an identity informationpair of a name and a mobile phone number of a user to be recognized isauthentic, thereby determining whether the to-be-recognized user'sidentity is fraudulent and thus detecting identity fraud.

In some embodiments, an identity recognition device is provided, asshown in FIG. 4. The device can include: a data collecting module 41, aninformation comparing module 42, and a risk determining module 43.

The data collecting module 41 is configured to collect big data ofaddress books from multiple users, each address book including multipleidentity information pairs, and each identity information paircomprising a name and a mobile phone number.

The searching module 42 is configured to search an identity informationpair to be recognized in the big data of address books, the identityinformation pair to be recognized including a name and a mobile phonenumber of a user to be recognized.

The risk determining module 43 is configured to determine that the userto be recognized is a user having a risk in response to that thesearching result satisfies a risk condition.

In some embodiments, as shown in FIG. 5, the searching module 42 in thedevice can include: a weight statistics obtaining unit 421 configured toperform statistical analysis on the identity information pairs in thebig data of address books to obtain an information weight correspondingto each identity information pair, the information weight being used toindicate a degree of credibility of the identity information pair; and aweight obtaining unit 422 configured to obtain an information weightcorresponding to the identity information pair to be recognized based onthe analysis.

The risk determining module 43 is configured to, for example, if theanalysis result does not have an information weight corresponding to theidentity information pair to be recognized, or if the information weightcorresponding to the identity information pair to be recognized is lowerthan a preset weight threshold, determine that the user to be recognizedis a user having a risk.

The weight statistics obtaining unit 421 is configured to, for example,use the number of address books including the identity information pairas an information weight of the identity information pair;alternatively, calculate a pagerank value of each identity informationpair using a pagerank method, and use the pagerank value as aninformation weight of the identity information pair.

In another example, the weight statistics obtaining unit 421 is furtherconfigured to correct inconsistency among identity information pairs indifferent address books before analyzing the identity information pairsin the big data of address books.

The identity recognition device creates an information weight tableaccording to big data of address books, determines credibility of eachidentity information pair, and can determine, based on a weightthreshold, whether an identity information pair of a name and a mobilephone number of a user to be recognized is authentic, therebydetermining whether the to-be-recognized user's identity is fraudulentand thus detecting identity fraud.

Embodiments are described above, which are not used to limit the presentdisclosure. Any modification, equivalent substitution or improvementmade within the spirit and principle of the present disclosure shall beencompassed by the protection scope of the present disclosure.

What is claimed is:
 1. An identity recognition method, comprising:collecting data of a plurality of address books, each address bookcomprising multiple identity information pairs of multiple users, andeach identity information pair comprising a name and a mobile phonenumber; correcting inconsistencies among identity information pairshaving a same mobile phone number and a different name in differentaddress books; after correcting the inconsistencies, performingstatistical analysis on the identity information pairs in the data ofaddress books to obtain an information weight corresponding to eachidentity information pair by calculating a pagerank value of eachidentity information pair using a pagerank method, and using thepagerank value as an information weight of the identity informationpair, wherein calculating the pagerank value of each identityinformation pair comprises determining a number of outbound links ofeach identity information pair, wherein each outbound link of anidentity information pair represents another identity information pairin the address book of a user to which the identity information pairbelongs, the information weight indicating a degree of credibility ofthe identity information pair; searching for an identity informationpair to be recognized in the data of address books, the identityinformation pair to be recognized comprising the name and the mobilephone number of a user to be recognized; in response to finding theidentity information pair to be recognized in the data of the addressbooks, obtaining the information weight corresponding to the identityinformation pair to be recognized; and in response to the informationweight corresponding to the identity information pair to be recognizedbeing lower than a threshold, determining that an identity of the userto be recognized is fraudulent.
 2. The method according to claim 1,further comprising: calculating the pagerank value of each identityinformation pair further based on the weights of the other identityinformation pairs connected with the each identity information pair bythe one or more outbound links.
 3. A system comprising one or moreprocessors and one or more non-transitory computer-readable memoriescoupled to the one or more processors and configured with instructionsexecutable by the one or more processors to cause the one or moreprocessors to perform operations comprising: collecting data of aplurality of address books, each address book comprising multipleidentity information pairs of multiple users, and each identityinformation pair comprising a name and a mobile phone number; correctinginconsistencies among identity information pairs having a same mobilephone number and a different name in different address books; aftercorrecting the inconsistencies, performing statistical analysis on theidentity information pairs in the big data of address books to obtain aninformation weight corresponding to each identity information pair bycalculating a pagerank value of each identity information pair using apagerank method, and using the pagerank value as the information weightof the identity information pair, wherein calculating the pagerank valueof each identity information pair comprises determining a number ofoutbound links of each identity information pair, wherein each outboundlink of an identity information pair represents another identityinformation pair in the address book of a user to which the identityinformation pair belongs, the information weight indicating a degree ofcredibility of the identity information pair; searching for an identityinformation pair to be recognized in the data of address books, theidentity information pair to be recognized comprising the name and themobile phone number of a user to be recognized; in response to findingthe identity information pair to be recognized is found in the data ofthe address books, obtaining the information weight corresponding to theidentity information pair to be recognized; and in response to theinformation weight corresponding to the identity information pair to berecognized being lower than a threshold, determining that an identity ofthe user to be recognized is fraudulent.
 4. The system according toclaim 3, wherein the operations further comprise: calculating thepagerank value of each identity information pair further based on theweights of the other identity information pairs connected with the eachidentity information pair by the one or more outbound links.
 5. Anon-transitory computer-readable storage medium storing instructionsthat, when executed by a processor, cause the processor to performoperations comprising: collecting data of a plurality of address books,each address book comprising multiple identity information pairs ofmultiple users, and each identity information pair comprising a name anda mobile phone number; correcting inconsistencies among identityinformation pairs having a same mobile phone number and a different namein different address books; after correcting the inconsistencies,performing statistical analysis on the identity information pairs in thebig data of address books to obtain an information weight correspondingto each identity information pair by: calculating a pagerank value ofeach identity information pair using a pagerank method, and using thepagerank value as the information weight of the identity informationpair, wherein calculating a pagerank value of an identity informationpair comprises determining a number of outbound links of each identityinformation pair, wherein each outbound link of an identity informationpair represents another identity information pair in the address book ofa user to which the identity information pair belongs, the informationweight indicating a degree of credibility of the identity informationpair; searching for an identity information pair to be recognized in thedata of address books, the identity information pair to be recognizedcomprising the name and the mobile phone number of a user to berecognized; in response to that the identity information pair to berecognized is found in the data of address books, obtaining theinformation weight corresponding to the identity information pair to berecognized; and in response to that the information weight correspondingto the identity information pair to be recognized is lower than athreshold, determining that an identity of the user to be recognized isfraudulent.
 6. The non-transitory computer-readable storage mediumaccording to claim 5, the operations further comprising: calculating thepagerank value of each identity information pair further based on theweights of the other identity information pairs connected with the eachidentity information pair by the one or more outbound links.